Analysis of cancer-derived extracellular vesicles (EVs) in biofluids potentially provides a source of disease biomarkers. At present there is no procedure to systematically identify which antigens should be targeted to differentiate cancer-derived from normal host cell-derived EVs. Here, we propose a computational framework that integrates information about membrane proteins in tumors and normal tissues from databases: UniProt, The Cancer Genome Atlas, the Genotype-Tissue Expression Project, and the Human Protein Atlas. We developed two methods to assess capture of EVs from specific cell types. (1) We used palmitoylated fluorescent protein (palmtdTomato) to label tumor-derived EVs. Beads displaying antibodies of interest were incubated with conditioned medium from palmtdTomato-expressing cells. Bound EVs were quantified using flow cytometry. (2) We also showed that membrane-bound Gaussia luciferase allows the detection of cancer-derived EVs in blood of tumor-bearing animals. Our analytical and validation platform should be applicable to identify antigens on EVs from any tumor type.
Extracellular vesicles (EVs) released by cells play a role in intercellular communication. Reporter and targeting proteins can be modified and exposed on the surface of EVs to investigate their half-life and biodistribution. A characterization of membrane-bound Gaussia luciferase (mbGluc) revealed that its signal was detected also in a form smaller than common EVs (<70 nm). We demonstrated that mbGluc initially exposed on the surface of EVs, likely undergoes proteolytic cleavage and processed fragments of the protein are released into the extracellular space in active form. Based on this observation, we developed a new assay to quantitatively track shedding of membrane proteins from the surface of EVs. We used this assay to show that ectodomain shedding in EVs is continuous and is mediated by specific proteases, e.g. metalloproteinases. Here, we present a novel tool to study membrane protein cleavage and release using both in vitro and in vivo models.